AI Engineer

Terranova•Berkeley, CA

About The Position

Terranova is a hard-tech company developing systems that combine mechanical engineering, robotics, automation, and real-world field operations. The AI Engineer will develop practical AI-powered tools, software, and analytical models to improve how the company designs, builds, tests, and operates complex machinery. This hands-on role focuses on solving engineering and operational problems in collaboration with mechanical, electrical, software, and field engineers. The engineer will identify inefficiencies, automate repetitive workflows, and turn operational data into actionable insights. The role calls for independently identifying opportunities, rapidly prototyping solutions, and deploying tools that measurably improve company operations. The position offers ownership of projects and the opportunity to apply AI to real-world engineering challenges, with solutions implemented in the shop and in the field.

Requirements

Strong proficiency in Python and experience building software tools, automation scripts, and data-processing pipelines.
Experience working with LLM APIs, AI agents, machine learning frameworks, or applied AI systems.
Ability to build end-to-end applications, from data ingestion and backend logic to simple user interfaces.
Strong data analysis skills, including SQL, Pandas, and statistical modeling.
Experience integrating APIs, databases, and third-party software platforms.
Comfort working with messy, incomplete, or unstructured real-world data.
Strong problem-solving skills and ability to independently turn loosely defined problems into working solutions.
Interest in physical engineering systems, manufacturing, robotics, or industrial operations.
Ability to work closely with multidisciplinary teams and communicate technical concepts clearly.

Nice To Haves

Experience with industrial automation, PLCs, sensors, or equipment telemetry.
Familiarity with mechanical engineering, fluid systems, hydraulics, or manufacturing processes.
Experience with time-series data, predictive modeling, or optimization algorithms.
Experience developing computer vision applications for industrial monitoring or quality control.
Familiarity with CAD software, engineering simulations, or engineering data pipelines.
Experience deploying internal tools using modern web frameworks and cloud services.

Responsibilities

Design and build internal AI-powered tools to streamline engineering, manufacturing, and operational workflows.
Identify repetitive, manual, or cumbersome processes and develop software solutions to automate them.
Integrate AI tools with existing engineering software, databases, documentation, and operational systems.
Build internal applications that help engineers and operators access information, make decisions, and complete tasks efficiently.
Develop systems to collect, track, and analyze metrics across shop operations, equipment performance, and field activities.
Analyze field injection data, including flow rates, pressures, material consumption, ground uplift, and operational efficiency.
Build dashboards, reporting tools, and data pipelines to visualize performance and identify improvement opportunities.
Quantify engineering and manufacturing processes to establish benchmarks, monitor performance, and reduce inefficiencies.
Develop models to predict operational outcomes, improve process parameters, and support engineering decisions.
Build and deploy machine learning, statistical, and predictive models for engineering applications.
Use LLMs, AI agents, and emerging technologies to automate complex workflows and improve internal productivity.
Develop tools to analyze experimental results, identify trends, and optimize system performance.
Explore AI applications across robotics, manufacturing, equipment monitoring, and field operations.
Work with engineers, technicians, and operators to understand challenges and develop practical solutions.
Take projects from problem identification through prototyping, implementation, and iteration.
Build lightweight, maintainable tools that can be deployed quickly and improved using real-world feedback.
Proactively identify opportunities where software, data, or AI can improve company efficiency and capabilities.